![]() |
Postdoctoral Fellow
Email: penny.ling.pan [@] gmail [DOT] com |
I am a postdoctoral fellow at MILA supervised by Prof. Yoshua Bengio. Prior to that, I received my Ph.D. from the Institute for Interdisciplinary Information Sciences (IIIS) (headed by Prof. Andrew Yao), Tsinghua University in 2022, advised by Prof. Longbo Huang. I received my B.E. from the School of Computer Science and Engineering (ranking 1/435 for four consecutive years during my undergraduate study), Sun Yat-Sen (Zhongshan) University, Guangzhou, China in 2017.
I visited Stanford University from April 2021 to January 2022, advised by Prof. Tengyu Ma. I visited University of Oxford from September 2020 to January 2021, advised by Prof. Shimon Whiteson. I was a research intern in the Machine Learning Group at Microsoft Research Asia from July 2018 to March 2019, advised by Dr. Wei Chen. I was also a recepient of Microsoft Research Asia Fellowship (2020). Please feel free to drop me an email if you are interested in collaborating with me.My research interests mainly include theoretical understanding, algorithmic improvements and practical application of generative flow networks (GFlowNets), reinforcement learning and multi-agent systems. I focus on developing robust, efficient, and practical deep reinforcement learning algorithms. I am also interested in the application of reinforcement learning in practical problems like computational sustainability and drug discovery.
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio
Preprint
[PDF]
Stochastic Generative Flow Networks
Ling Pan*, Dinghuai Zhang*, Moksh Jain, Longbo Huang, Yoshua Bengio
Preprint
[PDF]
Distributional GFlowNets with Quantile Flows
Dinghuai Zhang*, Ling Pan*, Ricky T.Q. Chen, Aaron Courville, Yoshua Bengio
Preprint
[PDF]
Generative Augmented Flow Networks
Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, Yoshua Bengio
In Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023
Spotlight (Top 5%)
[PDF] [Code]
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Yiqin Tan*, Pihe Hu*, Ling Pan, Jiatai Huang, Longbo Huang
In Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023
Spotlight (Top 5%)
[PDF] [Code]
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance
Can Chang, Ni Mu, Jiajun Wu, Ling Pan, Huazhe Xu
In Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022
Spotlight (Top 5%)
[PDF] [Website]
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu
In Thirty-Ninth International Conference on Machine Learning (ICML), Baltimore, USA, 2022
[PDF] [Code] [Website]
Recurrent Softmax Policy Gradient for Delay-Constrained Scheduling
Pihe Hu, Ling Pan, Yu Chen, Zhixuan Fang, Longbo Huang
In Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Seoul, South Korea, 2022
[PDF]
Regularized Softmax Deep Multi-Agent Q-Learning
Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson
In Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS), 2021
[PDF][Code]
Exploration in Policy Optimization through Multiple Paths
Ling Pan, Qingpeng Cai, Longbo Huang
Journal of Autonomous Agents and Multi-agent Systems (JAAMAS), 2021
[PDF]
Softmax Deep Double Deterministic Policy Gradients
Ling Pan, Qingpeng Cai, Longbo Huang
In Thirty-Fourth Conference on Neural Information Processing Systems (NeurIPS), 2020
[PDF][Code]
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Longbo Huang
In Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI), 2020, Yokohama, Japan
(Acceptance rate: 12.6%)
[PDF]
Multi-Path Policy Optimization
Ling Pan, Qingpeng Cai, Longbo Huang
In Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2020, Auckland, New Zealand
Invited for fast-track publication in JAAMAS (Top 5%)
[PDF]
Deterministic Value-Policy Gradients
Qingpeng Cai*, Ling Pan*, Pingzhong Tang
In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020, New York, USA
[PDF]
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
Ling Pan, Qingpeng Cai, Zhixuan Fang, Pingzhong Tang, Longbo Huang
In Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019, Hawaii, USA
(Acceptance rate: 16.2%)
[PDF] [Slides] [Poster]
Outstanding Doctoral Thesis, by Tsinghua University, 2022
Thesis: Towards Robust, Efficient, and Practical Deep Reinforcement Learning Algorithms
Outstanding Graduate (top 3%), by Tsinghua University, 2022
Also Beijing outstanding graduate and IIIS, Tsinghua University outstanding graduate, 2022
China National Scholarship (top 2%), by Ministry of Education of China, 2021
Microsoft Research Asia Fellowship, 2020
12 outstanding Ph.D. students in computer science in the Asia-Pacific region
China National Scholarship (top 2%), by Ministry of Education of China, 2016
China National Scholarship (top 2%), by Ministry of Education of China, 2015
China National Scholarship (top 2%), by Ministry of Education of China, 2014
SPC member:
PC member/Reviewer:
Towards Robust, Efficient, and Practical Reinforcement Learning Computer Science and Artificial Intelligence Lab (CSAIL), MIT, December, 2021 Berkeley Artificial Intelligence Research (BAIR), UC Berkeley, November, 2021
Regularized Softmax Deep Multi-Agent Q-Learning Reinforcement Learning China Community (RLChina), May, 2022 AI Time NeurIPS Session (by Tsinghua University), February, 2022 Third International Conference on Distributed Artificial Intelligence, January, 2022
Softmax Deep Double Deterministic Policy Gradients IJCAI-Shanghai Artificial Intelligence Industry Association (SAIA) Young Elite Symposium, July, 2021 Second International Conference on Distributed Artificial Intelligence, October, 2020
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems First International Conference on Distributed Artificial Intelligence, October, 2019 Nanjing University, May, 2019